"""simple docstring"""



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
   from sagemaker import Session, TrainingJobAnalytics
   from sagemaker.huggingface import HuggingFace






@pytest.mark.skipif(
    literal_eval(os.getenv("""TEST_SAGEMAKER""",						"""False"""       )       ) is not True,						reason="""Skipping test because should only be run when releasing minor transformers version""",						)
@pytest.mark.usefixtures("""sm_env"""       )
@parameterized_class(
    [
        {
            """framework""": """pytorch""",
            """script""": """run_glue.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]       )
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        if self.framework == "pytorch":
               subprocess.run(
                   F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split()    ,		encoding="utf-8"    ,		check=lowerCamelCase    ,		)
        assert hasattr(self    ,		"env"						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
        # distributed data settings
        __a       =    {"smdistributed": {"dataparallel": {"enabled": True}}} if self.script != "run_ddp.py" else None

        # creates estimator
        return HuggingFace(
            entry_point=self.script    ,		source_dir=self.env.test_path    ,		role=self.env.role    ,		image_uri=self.env.image_uri    ,		base_job_name=lowerCamelCase    ,		instance_count=lowerCamelCase    ,		instance_type=self.instance_type    ,		debugger_hook_config=lowerCamelCase    ,		hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}    ,		metric_definitions=self.env.metric_definitions    ,		distribution=lowerCamelCase    ,		py_version="py36"    ,		)


 def 						a__   ( self    ,		lowerCamelCase						):
        TrainingJobAnalytics(lowerCamelCase						).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv"						)






 @parameterized.expand([(2,)]						)
 def 						a__   ( self    ,		lowerCamelCase						):
        # create estimator
        __a       =    self.create_estimator(lowerCamelCase						)

        # run training
        estimator.fit()

        # result dataframe
        __a       =    TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

        # extract kpis
        __a       =    list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
        __a       =    list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
        # get train time from SageMaker job, this includes starting, preprocessing, stopping
        __a       =    (
            Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"    ,		999999						)
        )

        # assert kpis
        assert train_runtime <= self.results["train_runtime"]
        assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy						)
        assert all(t <= self.results["eval_loss"] for t in eval_loss						)

        # dump tests result into json file to share in PR
        with open(F"{estimator.latest_training_job.name}.json"    ,		"w"						) as outfile:
               json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}    ,		lowerCamelCase						)

"""simple docstring"""



from __future__ import annotations

from math import pi



def 	_lowerCamelCase(	a   ,    a   ,    a				):

       if (inductance, frequency, reactance).count(0				) != 1:
              raise ValueError("One and only one argument must be 0"				)
       if inductance < 0:
              raise ValueError("Inductance cannot be negative"				)
       if frequency < 0:
              raise ValueError("Frequency cannot be negative"				)
       if reactance < 0:
              raise ValueError("Inductive reactance cannot be negative"				)
       if inductance == 0:
              return {"inductance": reactance / (2 * pi * frequency)}
       elif frequency == 0:
              return {"frequency": reactance / (2 * pi * inductance)}
       elif reactance == 0:
              return {"reactance": 2 * pi * frequency * inductance}
       else:
              raise ValueError("Exactly one argument must be 0"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
